NumPy is a Python library that provides multi-dimensional array and matrix objects to handle large amounts of numerical data efficiently. It contains a powerful N-dimensional array object called ndarray that facilitates fast operations on large data sets. NumPy arrays can have any number of dimensions and elements of the array can be of any Python data type. NumPy also provides many useful methods for fast mathematical and statistical operations on arrays like summing, averaging, standard deviation, slicing, and matrix multiplication.
Vectorization refers to performing operations on entire NumPy arrays or sequences of data without using explicit loops. This allows computations to be performed more efficiently by leveraging optimized low-level code. Traditional Python code may use loops to perform operations element-wise, whereas NumPy allows the same operations to be performed vectorized on entire arrays. Broadcasting rules allow operations between arrays of different shapes by automatically expanding dimensions. Vectorization is a key technique for speeding up numerical Python code using NumPy.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
This document provides an overview of data analysis and visualization techniques using Python. It begins with an introduction to NumPy, the fundamental package for numerical computing in Python. NumPy stores data efficiently in arrays and allows for fast operations on entire arrays. The document then covers Pandas, which builds on NumPy and provides data structures like Series and DataFrames for working with structured and labeled data. It demonstrates how to load data, select subsets of data, and perform operations like filtering and aggregations. Finally, it discusses various data visualization techniques using Matplotlib and Seaborn like histograms, scatter plots, box plots, and heatmaps that can be used for exploratory data analysis to gain insights from data.
Matplotlib adalah pustaka plotting 2D Python yang menghasilkan gambar berkual...HendraPurnama31
Matplotlib adalah pustaka plotting 2D Python yang menghasilkan gambar berkualitas publikasi dalam berbagai format cetak dan lingkungan interaktif di berbagai platform.
This document discusses popular Python libraries for machine learning: Numpy, Pandas, and Matplotlib. Numpy provides multidimensional arrays and functions for working with large datasets. Pandas allows working with labeled data frames and series. Matplotlib is used for visualizing data through plots, histograms, and other charts. Key features of each library are described through examples of array creation, selection, and basic plotting functions.
NumPy provides two fundamental objects for multi-dimensional arrays: the N-dimensional array object (ndarray) and the universal function object (ufunc). An ndarray is a homogeneous collection of items indexed using N integers. The shape and data type define an ndarray. NumPy arrays have a dtype attribute that returns the data type layout. Arrays can be created using the array() function and have various dimensions like 0D, 1D, 2D and 3D.
NumPy is a Python package that provides multidimensional array and matrix objects as well as tools to work with these objects. It was created to handle large, multi-dimensional arrays and matrices efficiently. NumPy arrays enable fast operations on large datasets and facilitate scientific computing using Python. NumPy also contains functions for Fourier transforms, random number generation and linear algebra operations.
Basic of array and data structure, data structure basics, array, address calc...nsitlokeshjain
The document discusses different data structures and algorithms. It defines data structures as ways to store and organize data, mentioning linear structures like arrays and linked lists, and non-linear structures like trees and graphs. It also discusses abstract data types, arrays, sorting algorithms like bubble sort, and searching algorithms like linear and binary search. Key operations and their implementations are provided for each concept through definitions, examples and pseudocode.
This document provides a summary of key aspects of NumPy, the fundamental package for scientific computing in Python. It introduces NumPy ndarrays as a more efficient way to store and manipulate numerical data compared to built-in Python data types. It then covers how to create ndarrays, their basic properties like shape and dtype, and common operations like slicing, sorting, random number generation, and aggregations.
1. NumPy is a fundamental Python library for numerical computing that provides support for arrays and vectorized computations.
2. Pandas is a popular Python library for data manipulation and analysis that provides DataFrame and Series data structures to work with tabular data.
3. When performing arithmetic operations between DataFrames or Series in Pandas, the data is automatically aligned based on index and column labels to maintain data integrity. NumPy also automatically broadcasts arrays during arithmetic to align dimensions element-wise.
This document provides an overview of NumPy arrays, including how to create and manipulate vectors (1D arrays) and matrices (2D arrays). It discusses NumPy data types and shapes, and how to index, slice, and perform common operations on arrays like summation, multiplication, and dot products. It also compares the performance of vectorized NumPy operations versus equivalent Python for loops.
NumPy is a Python package that is used for scientific computing and working with multidimensional arrays. It allows fast operations on arrays through the use of n-dimensional arrays and has functions for creating, manipulating, and transforming NumPy arrays. NumPy arrays can be indexed, sliced, and various arithmetic operations can be performed on them element-wise for fast processing of large datasets.
Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
This document provides an overview of data analysis and visualization techniques using Python. It begins with an introduction to NumPy, the fundamental package for numerical computing in Python. NumPy stores data efficiently in arrays and allows for fast operations on entire arrays. The document then covers Pandas, which builds on NumPy and provides data structures like Series and DataFrames for working with structured and labeled data. It demonstrates how to load data, select subsets of data, and perform operations like filtering and aggregations. Finally, it discusses various data visualization techniques using Matplotlib and Seaborn like histograms, scatter plots, box plots, and heatmaps that can be used for exploratory data analysis to gain insights from data.
Matplotlib adalah pustaka plotting 2D Python yang menghasilkan gambar berkual...HendraPurnama31
Matplotlib adalah pustaka plotting 2D Python yang menghasilkan gambar berkualitas publikasi dalam berbagai format cetak dan lingkungan interaktif di berbagai platform.
This document discusses popular Python libraries for machine learning: Numpy, Pandas, and Matplotlib. Numpy provides multidimensional arrays and functions for working with large datasets. Pandas allows working with labeled data frames and series. Matplotlib is used for visualizing data through plots, histograms, and other charts. Key features of each library are described through examples of array creation, selection, and basic plotting functions.
NumPy provides two fundamental objects for multi-dimensional arrays: the N-dimensional array object (ndarray) and the universal function object (ufunc). An ndarray is a homogeneous collection of items indexed using N integers. The shape and data type define an ndarray. NumPy arrays have a dtype attribute that returns the data type layout. Arrays can be created using the array() function and have various dimensions like 0D, 1D, 2D and 3D.
NumPy is a Python package that provides multidimensional array and matrix objects as well as tools to work with these objects. It was created to handle large, multi-dimensional arrays and matrices efficiently. NumPy arrays enable fast operations on large datasets and facilitate scientific computing using Python. NumPy also contains functions for Fourier transforms, random number generation and linear algebra operations.
Basic of array and data structure, data structure basics, array, address calc...nsitlokeshjain
The document discusses different data structures and algorithms. It defines data structures as ways to store and organize data, mentioning linear structures like arrays and linked lists, and non-linear structures like trees and graphs. It also discusses abstract data types, arrays, sorting algorithms like bubble sort, and searching algorithms like linear and binary search. Key operations and their implementations are provided for each concept through definitions, examples and pseudocode.
This document provides a summary of key aspects of NumPy, the fundamental package for scientific computing in Python. It introduces NumPy ndarrays as a more efficient way to store and manipulate numerical data compared to built-in Python data types. It then covers how to create ndarrays, their basic properties like shape and dtype, and common operations like slicing, sorting, random number generation, and aggregations.
1. NumPy is a fundamental Python library for numerical computing that provides support for arrays and vectorized computations.
2. Pandas is a popular Python library for data manipulation and analysis that provides DataFrame and Series data structures to work with tabular data.
3. When performing arithmetic operations between DataFrames or Series in Pandas, the data is automatically aligned based on index and column labels to maintain data integrity. NumPy also automatically broadcasts arrays during arithmetic to align dimensions element-wise.
This document provides an overview of NumPy arrays, including how to create and manipulate vectors (1D arrays) and matrices (2D arrays). It discusses NumPy data types and shapes, and how to index, slice, and perform common operations on arrays like summation, multiplication, and dot products. It also compares the performance of vectorized NumPy operations versus equivalent Python for loops.
NumPy is a Python package that is used for scientific computing and working with multidimensional arrays. It allows fast operations on arrays through the use of n-dimensional arrays and has functions for creating, manipulating, and transforming NumPy arrays. NumPy arrays can be indexed, sliced, and various arithmetic operations can be performed on them element-wise for fast processing of large datasets.
This document provides an introduction and overview of natural language processing (NLP). It discusses how NLP aims to allow computers to communicate with humans using everyday language. It also discusses related areas like artificial intelligence, linguistics, and cognitive science. The document outlines some key aspects of communication like intention, generation, perception, analysis, and incorporation. It discusses the roles of syntax, semantics, and pragmatics. It also covers challenges in NLP like ambiguity and how ambiguity is pervasive and can lead to many possible interpretations. The document contrasts natural languages with computer languages and provides examples of common NLP tasks.
This document discusses database security. It introduces common threats to databases like loss of confidentiality, integrity and availability. The key database security requirements are then outlined as confidentiality, integrity, availability and non-repudiation. Two main types of access control are described - discretionary access control (DAC) using privileges and mandatory access control (MAC) using security classifications. The role of the database administrator to implement access controls is also discussed.
RE-LIVE THE EUPHORIA!!!!
The Quiz club of PSGCAS brings to you a fun-filled breezy general quiz set from numismatics to sports to pop culture.
Re-live the Euphoria!!!
QM: Eiraiezhil R K,
BA Economics (2022-25),
The Quiz club of PSGCAS
Adam Grant: Transforming Work Culture Through Organizational PsychologyPrachi Shah
This presentation explores the groundbreaking work of Adam Grant, renowned organizational psychologist and bestselling author. It highlights his key theories on giving, motivation, leadership, and workplace dynamics that have revolutionized how organizations think about productivity, collaboration, and employee well-being. Ideal for students, HR professionals, and leadership enthusiasts, this deck includes insights from his major works like Give and Take, Originals, and Think Again, along with interactive elements for enhanced engagement.
How to Create Time Off Request in Odoo 18 Time OffCeline George
Odoo 18 provides an efficient way to manage employee leave through the Time Off module. Employees can easily submit requests, and managers can approve or reject them based on company policies.
Trends Spotting Strategic foresight for tomorrow’s education systems - Debora...EduSkills OECD
Deborah Nusche, Senior Analyst, OECD presents at the OECD webinar 'Trends Spotting: Strategic foresight for tomorrow’s education systems' on 5 June 2025. You can check out the webinar on the website https://oecdedutoday.com/webinars/ Other speakers included: Deborah Nusche, Senior Analyst, OECD
Sophie Howe, Future Governance Adviser at the School of International Futures, first Future Generations Commissioner for Wales (2016-2023)
Davina Marie, Interdisciplinary Lead, Queens College London
Thomas Jørgensen, Director for Policy Coordination and Foresight at European University Association
IDSP is a disease surveillance program in India that aims to strengthen/maintain decentralized laboratory-based IT enabled disease surveillance systems for epidemic prone diseases to monitor disease trends, and to detect and respond to outbreaks in the early phases swiftly.....
Diptera: The Two-Winged Wonders, The Fly Squad: Order Diptera.pptxArshad Shaikh
Diptera, commonly known as flies, is a large and diverse order of insects that includes mosquitoes, midges, gnats, and horseflies. Characterized by a single pair of wings (hindwings are modified into balancing organs called halteres), Diptera are found in almost every environment and play important roles in ecosystems as pollinators, decomposers, and food sources. Some species, however, are significant pests and disease vectors, transmitting diseases like malaria, dengue, and Zika virus.
How to Manage Maintenance Request in Odoo 18Celine George
Efficient maintenance management is crucial for keeping equipment and work centers running smoothly in any business. Odoo 18 provides a Maintenance module that helps track, schedule, and manage maintenance requests efficiently.
Completed Sunday 6/8. For Weekend 6/14 & 15th. (Fathers Day Weekend US.) These workshops are also timeless for future students TY. No admissions needed.
A 9th FREE WORKSHOP
Reiki - Yoga
“Intuition-II, The Chakras”
Your Attendance is valued.
We hit over 5k views for Spring Workshops and Updates-TY.
Thank you for attending our workshops.
If you are new, do welcome.
Grad Students: I am planning a Reiki-Yoga Master Course (As a package). I’m Fusing both together.
This will include the foundation of each practice. Our Free Workshops can be used with any Reiki Yoga training package. Traditional Reiki does host rules and ethics. Its silent and within the JP Culture/Area/Training/Word of Mouth. It allows remote healing but there’s limits As practitioners and masters, we are not allowed to share certain secrets/tools. Some content is designed only for “Masters”. Some yoga are similar like the Kriya Yoga-Church (Vowed Lessons). We will review both Reiki and Yoga (Master tools) in the Course upcoming.
S9/This Week’s Focus:
* A continuation of Intuition-2 Development. We will review the Chakra System - Our temple. A misguided, misused situation lol. This will also serve Attunement later.
Thx for tuning in. Your time investment is valued. I do select topics related to our timeline and community. For those seeking upgrades or Reiki Levels. Stay tuned for our June packages. It’s for self employed/Practitioners/Coaches…
Review & Topics:
* Reiki Is Japanese Energy Healing used Globally.
* Yoga is over 5k years old from India. It hosts many styles, teacher versions, and it’s Mainstream now vs decades ago.
* Anything of the Holistic, Wellness Department can be fused together. My origins are Alternative, Complementary Medicine. In short, I call this ND. I am also a metaphysician. I learnt during the 90s New Age Era. I forget we just hit another wavy. It’s GenZ word of Mouth, their New Age Era. WHOA, History Repeats lol. We are fusing together.
* So, most of you have experienced your Spiritual Awakening. However; The journey wont be perfect. There will be some roller coaster events. The perks are: We are in a faster Spiritual Zone than the 90s. There’s more support and information available.
(See Presentation for all sections, THX AGAIN.)
Coleoptera, commonly known as beetles, is the largest order of insects, comprising approximately 400,000 described species. Beetles can be found in almost every habitat on Earth, exhibiting a wide range of morphological, behavioral, and ecological diversity. They have a hardened exoskeleton, with the forewings modified into elytra that protect the hind wings. Beetles play important roles in ecosystems as decomposers, pollinators, and food sources for other animals, while some species are considered pests in agriculture and forestry.
Stewart Butler - OECD - How to design and deliver higher technical education ...EduSkills OECD
Stewart Butler, Labour Market Economist at the OECD presents at the webinar 'How to design and deliver higher technical education to develop in-demand skills' on 3 June 2025. You can check out the webinar recording via our website - https://oecdedutoday.com/webinars/ .
You can check out the Higher Technical Education in England report via this link 👉 - https://www.oecd.org/en/publications/higher-technical-education-in-england-united-kingdom_7c00dff7-en.html
You can check out the pathways to professions report here 👉 https://www.oecd.org/en/publications/pathways-to-professions_a81152f4-en.html
How to Create a Stage or a Pipeline in Odoo 18 CRMCeline George
In Odoo, the CRM (Customer Relationship Management) module’s pipeline is a visual representation of a company's sales process that helps sales teams track and manage their interactions with potential customers.
How to Create Quotation Templates Sequence in Odoo 18 SalesCeline George
In this slide, we’ll discuss on how to create quotation templates sequence in Odoo 18 Sales. Odoo 18 Sales offers a variety of quotation templates that can be used to create different types of sales documents.
Artificial intelligence Presented by JM.jmansha170
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2. Usage of Numpy for numerical Data
NumPy (Numerical Python) is a fundamental library for
numerical computing in Python. It provides support for arrays,
matrices, and a wide range of mathematical functions.
Key Features
• Efficient storage and manipulation of numerical data.
• Functions for array operations, linear algebra, and random
number generation.
3. NumPy
• Stands for Numerical Python
• Is the fundamental package required for high performance
computing and data analysis
• NumPy is so important for numerical computations in Python
is because it is designed for efficiency on large arrays of data.
• It provides
• ndarray for creating multiple dimensional arrays
• Internally stores data in a contiguous block of memory, independent of other
built-in Python objects, use much less memory than built-in Python sequences.
• Standard math functions for fast operations on entire arrays of data without
having to write loops
• NumPy Arrays are important because they enable you to express batch
operations on data without writing any for loops. We call this vectorization.
4. NumPy ndarray vs list
• One of the key features of NumPy is its N-dimensional array
object, or ndarray, which is a fast, flexible container for large
datasets in Python.
• Whenever you see “array,” “NumPy array,” or “ndarray” in
the text, with few exceptions they all refer to the same thing:
the ndarray object.
• NumPy-based algorithms are generally 10 to 100 times faster
(or more) than their pure Python counterparts and use
significantly less memory.
import numpy as np
my_arr = np.arange(1000000)
my_list = list(range(1000000))
5. ndarray
• ndarray is used for storage of homogeneous data
• i.e., all elements the same type
• Every array must have a shape and a dtype
• Supports convenient slicing, indexing and efficient vectorized
computation
import numpy as np
data1 = [6, 7.5, 8, 0, 1]
arr1 = np.array(data1)
print(arr1)
print(arr1.dtype)
print(arr1.shape)
print(arr1.ndim)
6. Creating ndarrays
Using list of lists
import numpy as np
data2 = [[1, 2, 3, 4], [5, 6, 7, 8]] #list of lists
arr2 = np.array(data2)
print(arr2.ndim) #2
print(arr2.shape) # (2,4)
7. Create a 2d array from a list of list
• You can pass a list of lists to create a matrix-like a 2d array.
In:
Out:
8. The dtype argument
• You can specify the data-type by setting the dtype() argument.
• Some of the most commonly used NumPy dtypes are: float, int, bool,
str, and object.
In:
Out:
9. The astype argument
• You can also convert it to a different data-type using the astype method.
In: Out:
• Remember that, unlike lists, all items in an array have to be of the same
type.
10. dtype=‘object’
• However, if you are uncertain about what data type your
array will hold, or if you want to hold characters and
numbers in the same array, you can set the dtype as 'object'.
In: Out:
11. The tolist() function
• You can always convert an array into a list using the tolist() command.
In: Out:
12. Inspecting a NumPy array
• There are a range of functions built into NumPy that allow you to
inspect different aspects of an array:
In:
Out:
15. Arithmatic with NumPy Arrays
• Arithmetic operations with scalars propagate the scalar argument
to each element in the array:
• Comparisons between arrays of the same size yield boolean
arrays:
arr = np.array([[1., 2., 3.], [4., 5., 6.]])
print(arr)
[[1. 2. 3.]
[4. 5. 6.]]
print(arr **2)
[[ 1. 4. 9.]
[16. 25. 36.]]
arr2 = np.array([[0., 4., 1.], [7., 2., 12.]])
print(arr2)
[[ 0. 4. 1.]
[ 7. 2. 12.]]
print(arr2 > arr)
[[False True False]
[ True False True]]
16. Extracting specific items from an array
• You can extract portions of the array using indices, much like when
you’re working with lists.
• Unlike lists, however, arrays can optionally accept as many
parameters in the square brackets as there are number of dimensions
In: Out:
17. Indexing and Slicing
• One-dimensional arrays are simple; on the surface they act
similarly to Python lists:
arr = np.arange(10)
print(arr) # [0 1 2 3 4 5 6 7 8 9]
print(arr[5]) #5
print(arr[5:8]) #[5 6 7]
arr[5:8] = 12
print(arr) #[ 0 1 2 3 4 12 12 12 8 9]
18. Indexing and Slicing
• As you can see, if you assign a scalar value to a slice, as in
arr[5:8] = 12, the value is propagated (or broadcasted) to the
entire selection.
• An important first distinction from Python’s built-in lists is that
array slices are views on the original array.
• This means that the data is not copied, and any modifications to the view will be
reflected in the source array.
arr = np.arange(10)
print(arr) # [0 1 2 3 4 5 6 7 8 9]
arr_slice = arr[5:8]
print(arr_slice) # [5 6 7]
arr_slice[1] = 12345
print(arr) # [ 0 1 2 3 4 5 12345 7 8 9]
arr_slice[:] = 64
print(arr) # [ 0 1 2 3 4 64 64 64 8 9]
19. Indexing
• In a two-dimensional array, the elements at each index are no
longer scalars but rather one-dimensional arrays:
• Thus, individual elements can be accessed recursively. But that is
a bit too much work, so you can pass a comma-separated list of
indices to select individual elements.
• So these are equivalent:
arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(arr2d[2]) # [7 8 9]
print(arr2d[0][2]) # 3
print(arr2d[0, 2]) #3
20. Activity 3
• Consider the two-dimensional array, arr2d.
• Write a code to slice this array to display the last column,
[[3] [6] [9]]
• Write a code to slice this array to display the last 2 elements of
middle array,
[5 6]
arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
21. Boolean indexing
• A boolean index array is of the same shape as the array-to-
be-filtered, but it only contains TRUE and FALSE values.
In: Out:
22. Pandas
• Pandas, like NumPy, is one of the most popular Python
libraries for data analysis.
• It is a high-level abstraction over low-level NumPy, which is
written in pure C.
• Pandas provides high-performance, easy-to-use data
structures and data analysis tools.
• There are two main structures used by pandas; data frames
and series.
23. Indices in a pandas series
• A pandas series is similar to a list, but differs in the fact that a series associates a label with
each element. This makes it look like a dictionary.
• If an index is not explicitly provided by the user, pandas creates a RangeIndex ranging from 0
to N-1.
• Each series object also has a data type.
In: Ou
t:
24. • As you may suspect by this point, a series has ways to extract all of
the values in the series, as well as individual elements by index.
In: Ou
t:
• You can also provide an index manually.
In:
Out:
25. • It is easy to retrieve several elements of a series by their indices or make
group assignments.
In:
Out:
26. Filtering and maths operations
• Filtering and maths operations are easy with Pandas as well.
In: Ou
t:
27. Pandas data frame
• Simplistically, a data frame is a table, with rows and columns.
• Each column in a data frame is a series object.
• Rows consist of elements inside series.
Case ID Variable one Variable two Variable 3
1 123 ABC 10
2 456 DEF 20
3 789 XYZ 30
28. Creating a Pandas data frame
• Pandas data frames can be constructed using Python dictionaries.
In:
Out:
29. • You can also create a data frame from a list.
In: Out:
30. • You can ascertain the type of a column with the type() function.
In:
Out:
31. • A Pandas data frame object as two indices; a column index and row
index.
• Again, if you do not provide one, Pandas will create a RangeIndex from
0 to N-1.
In:
Out:
32. • There are numerous ways to provide row indices explicitly.
• For example, you could provide an index when creating a data frame:
In: Out:
• or do it during runtime.
• Here, I also named the index ‘country
code’.
In:
Out:
33. • Row access using index can be performed in several ways.
• First, you could use .loc() and provide an index label.
• Second, you could use .iloc() and provide an index number
In: Out:
In: Out:
34. • A selection of particular rows and columns can be selected this way.
In: Out:
• You can feed .loc() two arguments, index list and column list, slicing operation
is supported as well:
In: Out:
37. Reading from and writing to a file
• Pandas supports many popular file formats including CSV, XML,
HTML, Excel, SQL, JSON, etc.
• Out of all of these, CSV is the file format that you will work with the
most.
• You can read in the data from a CSV file using the read_csv() function.
• Similarly, you can write a data frame to a csv file with the to_csv()
function.
38. • Pandas has the capacity to do much more than what we have
covered here, such as grouping data and even data
visualisation.
• However, as with NumPy, we don’t have enough time to cover
every aspect of pandas here.
39. Exploratory data analysis (EDA)
Exploring your data is a crucial step in data analysis. It involves:
• Organising the data set
• Plotting aspects of the data set
• Maybe producing some numerical summaries; central tendency
and spread, etc.
“Exploratory data analysis can never be the whole story, but
nothing else can serve as the foundation stone.”
- John Tukey.
40. Reading in the data
• First we import the Python packages we are going to use.
• Then we use Pandas to load in the dataset as a data frame.
NOTE: The argument index_col argument states that we'll treat the first
column of the dataset as the ID column.
NOTE: The encoding argument allows us to by pass an input error created
by special characters in the data set.
#18: As NumPy has been designed to be able to work with very large arrays, you could imagine performance
and memory problems if NumPy insisted on always copying data.
If you want a copy of a slice of an ndarray instead of a view, you
will need to explicitly copy the array—for example,
arr[5:8].copy().